Aiming at the disadvantages of the traditional error back propagation（ BP） neural network method has poor network accuracy and sensitive weight setting in traffic flow prediction

proposes a short-term traffic flow prediction method based on improved bat algorithm（ BA） to optimize BP neural network. Firstly

adaptive inertia weight and acceleration factor are introduced to optimize the original bat algorithm

to improve its convergence speed and optimizing precision. Secondly

the improved BA is used to optimize the weight and threshold parameters of BP neutral network and construct BA-BP model for short-term traffic flow prediction The experimental results show that of compared with the traditional BP

the mean absolute error of this method is reduced by 3. 078 5

and the mean square error is reduced by 4. 4710.